AIMC Topic: Metagenome

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Phy-PMRFI: Phylogeny-Aware Prediction of Metagenomic Functions Using Random Forest Feature Importance.

IEEE transactions on nanobioscience Apr 24, 2019
High-throughput sequencing techniques have accelerated functional metagenomics studies through the generation of large volumes of omics data. The integration of these data using computational approaches is potentially useful for predicting metagenomi...
Phylogeny Support Vector Machine Databases, Genetic Humans Algorithms High-Throughput Nucleotide Sequencing Metagenomics Metagenome Decision Trees Microbiota Pharynx
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MetaPheno: A critical evaluation of deep learning and machine learning in metagenome-based disease prediction.

Methods (San Diego, Calif.) Mar 16, 2019
The human microbiome plays a number of critical roles, impacting almost every aspect of human health and well-being. Conditions in the microbiome have been linked to a number of significant diseases. Additionally, revolutions in sequencing technology...
Metagenome Humans Algorithms Machine Learning Deep Learning Obesity Diabetes Mellitus, Type 2 Microbiota Metagenomics
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Using Unlabeled Data to Discover Bivariate Causality with Deep Restricted Boltzmann Machines.

IEEE/ACM transactions on computational biology and bioinformatics Nov 5, 2018
An important question in microbiology is whether treatment causes changes in gut flora, and whether it also affects metabolism. The reconstruction of causal relations purely from non-temporal observational data is challenging. We address the problem ...
Humans Databases, Genetic Causality Models, Statistical Neural Networks, Computer Computational Biology Gastrointestinal Microbiome Metagenome Supervised Machine Learning Metformin
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Detection of Colorectal Carcinoma Based on Microbiota Analysis Using Generalized Regression Neural Networks and Nonlinear Feature Selection.

IEEE/ACM transactions on computational biology and bioinformatics Sep 13, 2018
To obtain a screening tool for colorectal cancer (CRC) based on gut microbiota, we seek here to identify an optimal classifier for CRC detection as well as a novel nonlinear feature selection method for determining the most discriminative microbial s...
Gastrointestinal Microbiome Metagenome Feces Bacteria Female Aged Colorectal Neoplasms Male Middle Aged Diagnosis, Computer-Assisted Neural Networks, Computer Machine Learning Algorithms Humans
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Deep learning models for bacteria taxonomic classification of metagenomic data.

BMC bioinformatics Jul 9, 2018
BACKGROUND: An open challenge in translational bioinformatics is the analysis of sequenced metagenomes from various environmental samples. Of course, several studies demonstrated the 16S ribosomal RNA could be considered as a barcode for bacteria cla...
Metagenome Time Factors Algorithms RNA, Ribosomal, 16S Neural Networks, Computer Reproducibility of Results Bacteria Databases, Genetic Models, Genetic Deep Learning Metagenomics
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Phenotype Prediction from Metagenomic Data Using Clustering and Assembly with Multiple Instance Learning (CAMIL).

IEEE/ACM transactions on computational biology and bioinformatics Oct 4, 2017
The recent advent of Metagenome Wide Association Studies (MGWAS) provides insight into the role of microbes on human health and disease. However, the studies present several computational challenges. In this paper, we demonstrate a novel, efficient, ...
Humans Machine Learning Phenotype Cluster Analysis Metagenomics Metagenome
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Machine Learning Meta-analysis of Large Metagenomic Datasets: Tools and Biological Insights.

PLoS computational biology Jul 11, 2016
Shotgun metagenomic analysis of the human associated microbiome provides a rich set of microbial features for prediction and biomarker discovery in the context of human diseases and health conditions. However, the use of such high-resolution microbia...
Metagenome Gastrointestinal Microbiome Obesity Machine Learning Metagenomics Software Colorectal Neoplasms Inflammatory Bowel Diseases Computational Biology Humans
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Large-scale machine learning for metagenomics sequence classification.

Bioinformatics (Oxford, England) Nov 20, 2015
MOTIVATION: Metagenomics characterizes the taxonomic diversity of microbial communities by sequencing DNA directly from an environmental sample. One of the main challenges in metagenomics data analysis is the binning step, where each sequenced read i...
Software Algorithms Sequence Analysis, DNA Metagenomics Machine Learning Metagenome
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DectICO: an alignment-free supervised metagenomic classification method based on feature extraction and dynamic selection.

BMC bioinformatics Oct 7, 2015
BACKGROUND: Continual progress in next-generation sequencing allows for generating increasingly large metagenomes which are over time or space. Comparing and classifying the metagenomes with different microbial communities is critical. Alignment-free...
Metagenome Algorithms Support Vector Machine Bacteria Least-Squares Analysis Metagenomics
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MetaVelvet-SL: an extension of the Velvet assembler to a de novo metagenomic assembler utilizing supervised learning.

DNA research : an international journal for rapid publication of reports on genes and genomes Nov 27, 2014
The assembly of multiple genomes from mixed sequence reads is a bottleneck in metagenomic analysis. A single-genome assembly program (assembler) is not capable of resolving metagenome sequences, so assemblers designed specifically for metagenomics ha...
Metagenome Support Vector Machine Humans Sequence Analysis, DNA Software Genome
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